Wet and dry days#
Highlights
While variability is high, there has been little change in annual total rainfall observed in the meteorological station at Koror over the last 72 years
There has been little change in both in dry and wet conditions, exhibiting no statistically significant trend over the period 1951 to 2024.
Show code cell source
import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys
import folium
from myst_nb import glue
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.plotting import plot_bar_probs
from ind_setup.colors import get_df_col
from ind_setup.core import fontsize
from ind_setup.tables import plot_df_table, table_rain_a_summary
sys.path.append("../../../functions")
from data_downloaders import GHCN
country = 'Palau'
vars_interest = ['PRCP']
Get Data#
update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
Show code cell source
if update_data:
df_country = GHCN.get_country_code(country)
print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')
df_stations = GHCN.download_stations_info()
df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
print(f'There are {df_country_stations.shape[0]} stations in {country}')
Using Koror Station#
Show code cell source
if update_data:
GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
id = 'PSW00040309' # Koror Station
dict_prcp = GHCN.extract_dict_data_var(GHCND_dir, 'PRCP', df_country_stations.loc[df_country_stations['ID'] == id])[0]
data = dict_prcp[0]['data']#.dropna()
data.to_pickle(op.join(path_data, 'GHCN_precipitation.pkl'))
else:
data = pd.read_pickle(op.join(path_data, 'GHCN_precipitation.pkl'))
Analysis of wet days
dict_prcp = [{'data' : data, 'var' : 'PRCP', 'ax' : 1, 'label' : 'Precipitation'}, ]
Wet days#
Wet days are considered when rainfall is above 1mm
data = dict_prcp[0]['data']#.dropna()
data = data.groupby(data.index.year).filter(lambda x: len(x) >= 300).dropna()
glue("n_years", len(np.unique(data.index.year)), display=False)
data['wet_day'] = np.where(data['PRCP'] > 1, 1, np.where((np.isnan(data['PRCP'])==True), np.nan, 0))
fig, ax = plot_bar_probs(x = [0, 1], y = data.groupby('wet_day').count()['PRCP'].values, labels = ['Dry Days', 'Wet Days'])
ax.set_title('Distribution of Wet Days', fontsize = fontsize)
ax.set_ylabel('Number of Days', fontsize = fontsize)
Text(0, 0.5, 'Number of Days')
Accumulated precipitation#
# Correct accumulated precipitation with number of observations per year to make fair comparisons and trends
datag = (data.groupby(data.index.year).sum()/ data.groupby(data.index.year).count()) * 365
datag.index = pd.to_datetime(datag.index, format = '%Y')
dict_plot = [{'data' : datag, 'var' : 'PRCP', 'ax' : 1, 'label':'Accumulated precipitation [mm]'},]
fig, ax = plot_bar_probs(x = datag.index.year, y = datag['PRCP'].values,
trendline = True, figsize = [15, 4])
ax.set_ylabel('Accumulated Rainfall [mm]', fontsize = fontsize)
Text(0, 0.5, 'Accumulated Rainfall [mm]')
Number of days over and above 1mm threshold#
threshold = 1 #np.percentile(data['PRCP'].dropna(), 90)
data['wet_day_t'] = np.where(data['PRCP'] > threshold, 1, np.where((np.isnan(data['PRCP'])==True), np.nan, 0))
data_th = data.groupby([data.index.year, data.wet_day_t]).count()['PRCP']
data_th = data_th/data.groupby(data.index.year).count()['PRCP'] * 365
fig, ax = plt.subplots(figsize = [15, 5])
data_th.unstack().plot(kind = 'bar', stacked = True, ax = ax, color = get_df_col()[:2], edgecolor = 'white', alpha = .5)
ax.set_ylabel('Number of days', fontsize = fontsize)
Text(0, 0.5, 'Number of days')
#Wet days
data2 = data.loc[data['wet_day_t'] == 1]
data2 = data2.groupby(data2.index.year).count()
fig, ax, trend = plot_bar_probs(x = data2.index, y = data2.PRCP.values, trendline = True,
y_label = 'Number of wet days [>1mm]', figsize = [15, 4], return_trend = True)
plt.savefig(op.join(path_figs, 'F7a_Wet_days_1mm.png'), dpi=300, bbox_inches='tight')
glue("trend_wet", float(trend), display=False)
#Dry days
data2 = data.loc[data['wet_day_t'] == 0]
data2 = data2.groupby(data2.index.year).count()
fig, ax, trend = plot_bar_probs(x = data2.index, y = data2.PRCP.values, trendline = True,
y_label = 'Number of dry days [<1mm]', figsize = [15, 4], return_trend = True)
plt.savefig(op.join(path_figs, 'F6a_Number_dry.png'), dpi=300, bbox_inches='tight')
glue('trend_dry', float(trend), display=False)
Days over 95 threshold#
prcentile = 95
threshold = np.round(np.percentile(data['PRCP'].dropna(), prcentile), 2)
print(f'Threshold of {threshold}mm')
data['wet_day_t'] = np.where(data['PRCP'] > threshold, 1, np.where((np.isnan(data['PRCP'])==True), np.nan, 0))
Threshold of 45.7mm
data_2 = data.loc[data['wet_day_t'] == 1][['PRCP']]
data_over_th = data_2.groupby(data_2.index.year).count()
data_over_th.index = pd.to_datetime(data_over_th.index, format = '%Y')
data_over_th['PRCP_below'] = 365 - data_over_th['PRCP'].values
dict_plot = [{'data' : data_over_th, 'var' : 'PRCP', 'ax' : 1, 'label':f'Number of days over threshold: {threshold}mm'},]
fig = plot_bar_probs(x = data_over_th.index.year, y = data_over_th['PRCP'].values, trendline = True,
figsize = (15, 5), y_label = f'Number of days over {prcentile} percentile')
plt.savefig(op.join(path_figs, 'F7b_Wet_days_95p.png'), dpi=300, bbox_inches='tight')
Annual maxima#
data_max = data.groupby(data.index.year).max()
data_max.index = pd.to_datetime(data_max.index, format = '%Y')
dict_plot = [{'data' : data_max, 'var' : 'PRCP', 'ax' : 1, 'label':'Annual daily maxima'},]
plot_timeseries_interactive(dict_plot, trendline = True, figsize = (25, 12), label_yaxes = 'Precipitation [mm]');
df = table_rain_a_summary(data)
fig = plot_df_table(df.T, figsize = (600, 350),)
